Carbon pricing policies and renewable energy development: Analysis based on cross-country panel data.

While studies have theoretically discussed the impact of carbon pricing on renewable energy, the practical implementation and effectiveness of these policies remain uncertain. This study empirically examines the role of carbon emissions trading and carbon tax in global renewable energy development using panel data from 196 countries and regions and employing the staggered difference-in-differences (DID) model and Bacon decomposition method. The results suggest that: (1) From the perspective of policy shocks, carbon trading has increased non-hydro renewable electricity generation by 73.32%, while carbon tax has increased it by 31.79%. This indicates that the overall impact of carbon trading on renewable energy is greater than that of carbon tax. However, the elasticity coefficients of renewable energy to carbon trading prices and carbon tax rates are 0.1801 and 0.1845, respectively, suggesting a slightly greater marginal effect of carbon tax on renewable energy compared to carbon trading. (2) Both carbon tax and carbon trading have mitigated the growth of fossil electricity and encouraged public investment in renewable energy, thereby fostering its development. (3) The influence of carbon pricing on renewable energy varies by income level; notably, the implementation of these policies in high-income countries has diminished their promotional effect on renewable energy. (4) The contribution of technological innovation to renewable energy development is smaller than that of policies including carbon trading and carbon tax, indicating that renewable energy development during the sample period was predominantly driven by policy measures. The findings indicate that the application of carbon pricing policies should be further promoted to accelerate the energy mix transition.

Productive use of energy of women-owned micro-, small-, and medium-sized enterprises: Insights from food and textile businesses in selected African countries.

This paper presents a descriptive study focusing on the productive energy use of women-owned micro-, small-, and medium-sized enterprises that operate in Africa's food and textile sectors. Through a multidisciplinary approach, combining primary and secondary data collection methods, and integrating quantitative and qualitative tools, this study examines the relationship between the gender-based ownership structure of enterprises (i.e., sole female, female-female, and female-male) and energy consumption patterns, including demand levels, carrier use, access type (on-grid or off-grid), and expenditure. Despite limitations in scope and sample size, the findings shed light on gender-specific productive use practices. Findings show that female-owned businesses primarily rely on single or dual energy carriers, contrasting with female-male enterprises, which typically employ two or more energy carriers. Fuel usage varies among ownership structures, with diesel, biomass, and liquified petroleum gas being notable choices. Increasing diversity in ownership correlates with heightened awareness of energy metrics and monthly demand for electric and mechanical power, with some of the latter correlation also observed for thermal energy. Moreover, as ownership diversity increases, energy expenditure per kilogramme of production output decreases. Some sole female-owned enterprises surpass 100 USD/kg/month, female-female partnerships may reach 100 USD/kg/month, whereas female-male co-owned enterprises remain below 10 USD/kg/month. Beyond contributing to understanding gendered productive energy practices, this research also emphasises the importance of gender mainstreaming in productive use and energy access interventions. It highlights the need for renewable energy solutions, capacity-building programmes, and further research to address efficiency and accessibility challenges faced by women entrepreneurs.

Next-Generation Green Hydrogen: Progress and Perspective from Electricity, Catalyst to Electrolyte in Electrocatalytic Water Splitting.

Green hydrogen from electrolysis of water has attracted widespread attention as a renewable power source. Among several hydrogen production methods, it has become the most promising technology. However, there is no large-scale renewable hydrogen production system currently that can compete with conventional fossil fuel hydrogen production. Renewable energy electrocatalytic water splitting is an ideal production technology with environmental cleanliness protection and good hydrogen purity, which meet the requirements of future development. This review summarizes and introduces the current status of hydrogen production by water splitting from three aspects: electricity, catalyst and electrolyte. In particular, the present situation and the latest progress of the key sources of power, catalytic materials and electrolyzers for electrocatalytic water splitting are introduced. Finally, the problems of hydrogen generation from electrolytic water splitting and directions of next-generation green hydrogen in the future are discussed and outlooked. It is expected that this review will have an important impact on the field of hydrogen production from water.

Selection of renewable energy development path for sustainable development using a fuzzy MCDM based on cumulative prospect theory: the case of Malaysia.

Malaysia's excessive energy consumption has led to the depletion of traditional energy reserves such as oil and natural gas. Although Malaysia has implemented multiple policies to achieve sustainable national energy development, the current results are unsatisfactory. As of 2022, only 2% of the country's electricity supply comes from renewable energy, which accounts for less than 30% of the energy structure. Malaysia must ensure energy security and diversified energy supply while ensuring sustainable energy development. This article uses the fuzzy multi-criteria decision-making(MCDM) method based on cumulative prospect theory to help decision-makers choose the most suitable renewable energy for sustainable development in Malaysia from four dimensions of technology, economy, society, and environment. The results show that solar power is the most suitable renewable energy for sustainable development, followed by biomass, wind, and hydropower, but the optimal alternative is sensitive to the prospect parameters. Finally, it was analyzed that efficiency, payback period, employment creation, and carbon dioxide (CO2) emissions are the most critical factors affecting the development of renewable energy in Malaysia under the four dimensions. Reasonable suggestions are proposed from policy review, green finance, public awareness, engineering education, and future energy. This research provides insightful information that can help Malaysian decision-makers scientifically formulate Sustainable development paths for renewable energy, analyze the problems encountered in the current stage of renewable energy development, and provide recommendations for Malaysia's future renewable energy transition and sustainable development.

A reduced vector model predictive controller for a three-level neutral point clamped inverter with common-mode voltage suppression.

This paper presents a novel, state-of-the-art predictive control architecture that addresses the computational complexity and limitations of conventional predictive control methodologies while enhancing the performance efficacy of predictive control techniques applied to three-level voltage source converters (NPC inverters). This framework's main goal is to decrease the number of filtered voltage lifespan vectors in each sector, which will increase the overall efficiency of the control system and allow for common mode voltage reduction in three-level voltage source converters. Two particular tactics are described in order to accomplish this. First, a statistical approach is presented for the proactive detection of potential voltage vectors, with an emphasis on selecting and including the vectors that are most frequently used. This method lowers the computational load by limiting the search space needed to find the best voltage vectors. Then, using statistical analysis, a plan is presented to split the sectors into two separate parts, so greatly limiting the number of voltage vectors. The goal of this improved predictive control methodology is to reduce computing demands and mitigate common mode voltage. The suggested strategy's resilience is confirmed in a range of operational scenarios using simulations and empirical evaluation. The findings indicate a pronounced enhancement in computational efficiency and a notable diminution in common mode voltage, thereby underscoring the efficacy of the proposed methodology. This increases their ability to incorporate renewable energy sources into the electrical grid.

Utica/point pleasant brine isotopic compositions (δ7Li, δ11B, δ138Ba) elucidate mechanisms of lithium enrichment in the Appalachian Basin.

Global Li production will require a ~500 % increase to meet 2050 projected energy storage demands. One potential source is oil and gas wastewater (i.e., produced water or brine), which naturally has high total dissolved solids (TDS) concentrations, that can also be enriched in Li (>100 mg/L). Understanding the sources and mechanisms responsible for high naturally-occurring Li concentrations can aid in efficient targeting of these brines. The isotopic composition (δ7Li, δ11B, δ138Ba) of produced water and core samples from the Utica Shale and Point Pleasant Formation (UPP) in the Appalachian Basin, USA indicates that depth-dependent thermal maturity and water-rock interaction, including diagenetic clay mineral transformations, likely control Li concentrations. A survey of Li content in produced waters throughout the USA indicates that Appalachian Basin brines from the Marcellus Shale to the UPP have the potential for economic resource recovery.

Assessing CO2 sources and sinks in and around Taiwan: Implication for achieving regional carbon neutrality by 2050.

Taiwan has pledged to achieve net-zero carbon emissions by 2050, but the current extent of carbon sinks in Taiwan remains unclear. Therefore, this study aims to first review the existing nature-based carbon sinks on land and in the oceans around Taiwan. Subsequently, we suggest potential strategies to reduce CO2 emissions and propose carbon dioxide removal methods (CDRs). The natural carbon sinks by forests, sediments, and oceans in and around Taiwan are approximately 21.5, 42.1, and 96.8 Mt-CO2 y-1, respectively, which is significantly less than Taiwan's CO2 emissions (280 Mt-CO2 y-1). Taiwan must consider decarbonization strategies like using electric vehicles, renewable energy, and hydrogen energy by formulating enabling policies. Besides more precisely assessing both terrestrial and marine carbon sinks, Taiwan should develop novel CDRs such as bioenergy with carbon capture and storage, afforestation, reforestation, biochar, seaweed cultivation, and ocean alkalinity enhancement, to reach carbon neutrality by 2050.

Sustainable Valorisation of Sewage Sludge via Carbon Dioxide-Assisted Pyrolysis.

The escalating volume of sewage sludge (SS) generated poses challenges in disposal, given its potential harm to the environment and human health. This study explored sustainable solutions for SS management with a focus on energy recovery. Employing CO2-assisted pyrolysis, we converted SS into flammable gases (H2 and CO; syngas). Single-stage pyrolysis of SS in a CO2 conditions demonstrated that CO2 enhances flammable gas production (especially CO) through gas phase reactions (GPRs) with volatile matter (VM) at temperatures ≥ 520 ˚C. Specifically, the CO2 partially oxidized the VM released from SS and concurrently underwent reduction into CO. To enhance the syngas production at temperatures ≤ 520 ˚C, multi-stage pyrolysis setup with additional heat energy and a Ni/Al2O3 catalyst were utilized. These configurations significantly increased flammable gas production, particularly CO, at temperatures ≤ 520 ˚C. Indeed, the flammable gas yield in the catalytic pyrolysis of SS increased from 200.3 mmol under N2 conditions to 219.2 mmol under CO2 conditions, representing a 4.4-fold increase compared to single-stage pyrolysis under CO2 conditions (50.0 mmol). By integrating a water-gas-shift reaction, the flammable gases produced from CO2-assisted catalytic pyrolysis were expected to have the potential to generate revenue of US$4.04 billion. These findings highlight the effectiveness of employing CO2 in SS pyrolysis as a sustainable and effective approach for treating and valorising SS into valuable energy resources.

Assessment of wind energy resource in the western region of Somaliland.

As the population of Somaliland continues to grow rapidly, the demand for electricity is anticipated to rise exponentially over the next few decades. The provision of reliable and cost-effective electricity service is at the core of the economic and social development of Somaliland. Wind energy might offer a sustainable solution to the exceptionally high electricity prices. In this study, a techno-economic assessment of the wind energy potential in some parts of the western region of Somaliland is performed. Measured data of wind speed and wind direction for three sites around the capital city of Hargeisa are utilized to characterize the resource using Weibull distribution functions. Technical and economic performances of several commercial wind turbines are examined. Out of the three sites, Xumba Weyne stands out as the most favorable site for wind energy harnessing with average annual power and energy densities at 80 m hub height of 317 kW/m2 and 2782 kWh/m2, respectively. Wind turbines installed in Xumba Weyne yielded the lowest levelized cost of electricity (LCOE) of not more than 0.07 $/kWh, shortest payback times (i.e., less than 7.2 years) with minimum return on investment (ROI) of approximately 150%.

Life cycle and economic assessment of tidal energy farms in early design phases: Application to a second-generation tidal device.

Ocean currents are emerging as key contributors to renewable energy generation. However, technologies for harvesting tidal current energy are still in the early stages of development. In this context, environmental and economic studies on tidal energy converters (TECs) are crucial to further advance tidal technology and facilitate its entry into the market. This article presents a life cycle and economic assessment of a 34.5 MW tidal farm project comprising 23 second-generation tidal devices, each with a rated power of 1.5 MW. The tidal system was simulated using primary data from the full-scale floating platform Atir. The Atir is a pre-commercial tidal device designed with a steel trimaran and a submerged section for TEC installation. An assessment of 18 environmental impact categories was conducted using the ReCiPe 2016 MidPoint method, with process flow systems modelled using SimaPro v9.2.0.1 software. The environmental assessment indicates emissions of 42.11 g CO2eq per kWh, primarily stemming from manufacturing processes that demand substantial amounts of steel. The economic analysis reveals a Levelized Cost of Electricity (LCOE) of 0.125 EUR/kWh, consistent with European Commission projections. Although the platform structure represents a high initial investment, the lower maintenance costs of the Atir device provide long-term savings and, overall, result in a competitive LCOE. The study also introduces a methodological framework for harmonised environmental and economic assessments in tidal energy projects, proving crucial in supporting decision-making processes.

Climate Change and Health: Public Health and Legal Strategies to Reduce Reliance on Fossil Fuels, Increase Air Quality, and Improve Human Health.

Reliance upon fossil fuels and limited greenspace contribute to poor indoor and outdoor air quality and adverse health outcomes, particularly in communities of color. This article describes justice-informed public health and legal interventions to increase access to greenspace and accelerate the transitions to renewable energy and away from gas appliances.

A hierarchical optimization approach to maximize hosting capacity for electric vehicles and renewable energy sources through demand response and transmission expansion planning.

Within the scope of sustainable development, integrating electric vehicles (EVs) and renewable energy sources (RESs) into power grids offers a number of benefits. These include reducing greenhouse gas emissions, diversifying energy sources, and promoting the use of green energy. Although the literature on hosting capacity (HC) models has grown, there is still a noticeable gap in the discussion of models that successfully handle transmission expansion planning (TEP), demand response (DR), and HC objectives simultaneously. Combining TEP, DR, and HC objectives in one model optimizes resource use, enhances grid stability, supports renewable and EV integration, and aligns with regulatory and market demands, resulting in a more efficient, reliable, and sustainable power system. This research presents an innovative two-layer HC model, including considerations for TEP and DR. The model determines the highest degree of load shifting appropriate for incorporation into power networks in the first layer. Meanwhile, the second layer focuses on augmenting the RES and EVs' hosting capability and modernizing the network infrastructure. System operators can choose the best scenario to increase the penetration level of EVs and RESs with the aid of the proposed model. The proposed model, which is formulated as a multi-objective mixed-integer nonlinear optimization problem, uses a hierarchical optimization technique to identify effective solutions by combining the particle swarm optimization algorithm and the crayfish optimizer. When compared to traditional methods, the results obtained from implementing the proposed hierarchical optimization algorithm on the Garver network and the IEEE 24-bus system indicated how effective it is at solving the presented HC model. The case studies demonstrated that integrating DR into the HC problem reduced peak load by 10.4-23.25%. The findings also highlighted that DR did not impact the total energy consumed by EVs throughout the day, but it did reshape the timing of EV charging, creating more opportunities for integration during periods of high demand. Implementing DR reduced the number of projects needed and, in some cases, led to cost savings of up to 12.3%.

Multi-objective energy management in a renewable and EV-integrated microgrid using an iterative map-based self-adaptive crystal structure algorithm.

The use of plug-in hybrid electric vehicles (PHEVs) provides a way to address energy and environmental issues. Integrating a large number of PHEVs with advanced control and storage capabilities can enhance the flexibility of the distribution grid. This study proposes an innovative energy management strategy (EMS) using an Iterative map-based self-adaptive crystal structure algorithm (SaCryStAl) specifically designed for microgrids with renewable energy sources (RESs) and PHEVs. The goal is to optimize multi-objective scheduling for a microgrid with wind turbines, micro-turbines, fuel cells, solar photovoltaic systems, and batteries to balance power and store excess energy. The aim is to minimize microgrid operating costs while considering environmental impacts. The optimization problem is framed as a multi-objective problem with nonlinear constraints, using fuzzy logic to aid decision-making. In the first scenario, the microgrid is optimized with all RESs installed within predetermined boundaries, in addition to grid connection. In the second scenario, the microgrid operates with a wind turbine at rated power. The third case study involves integrating plug-in hybrid electric vehicles (PHEVs) into the microgrid in three charging modes: coordinated, smart, and uncoordinated, utilizing standard and rated RES power. The SaCryStAl algorithm showed superior performance in operation cost, emissions, and execution time compared to traditional CryStAl and other recent optimization methods. The proposed SaCryStAl algorithm achieved optimal solutions in the first scenario for cost and emissions at 177.29 €ct and 469.92 kg, respectively, within a reasonable time frame. In the second scenario, it yielded optimal cost and emissions values of 112.02 €ct and 196.15 kg, respectively. Lastly, in the third scenario, the SaCryStAl algorithm achieves optimal cost values of 319.9301 €ct, 160.9827 €ct and 128.2815 €ct for uncoordinated charging, coordinated charging and smart charging modes respectively. Optimization results reveal that the proposed SaCryStAl outperformed other evolutionary optimization algorithms, such as differential evolution, CryStAl, Grey Wolf Optimizer, particle swarm optimization, and genetic algorithm, as confirmed through test cases.

Backward neural network (BNN) based multilevel control for enhancing the quality of an islanded RES DC microgrid under variable communication network.

Microgrids (MGs) and energy communities have been widely implemented, leading to the participation of multiple stakeholders in distribution networks. Insufficient information infrastructure, particularly in rural distribution networks, is leading to a growing number of operational blind areas in distribution networks. An optimization challenge is addressed in multi-feeder microgrid systems to handle load sharing and voltage management by implementing a backward neural network (BNN) as a robust control approach. The control technique consists of a neural network that optimizes the control strategy to calculate the operating directions for each distributed generating point. Neural networks improve control during communication connectivity issues to ensure the computation of operational directions. Traditional control of DC microgrids is susceptible to communication link delays. The proposed BNN technique can be expanded to encompass the entire multi-feeder network for precise load distribution and voltage management. The BNN results are achieved through mathematical analysis of different load conditions and uncertain line characteristics in a radial network of a multi-feeder microgrid, demonstrating the effectiveness of the proposed approach. The proposed BNN technique is more effective than conventional control in accurately distributing the load and regulating the feeder voltage, especially during communication failure.

A review on the energy in buildings: Current research focus and future development direction.

Currently, although energy conservation related research in buildings is a matter of great urgency in the context of an ever more serious energy crisis, people seem to pay more attention on the field of civil engineering, such as the design, construction, monitoring and maintenance management of building structures. This is also evidenced by the authors' extensive research and strong practical engineering experience in infrastructure projects such as bridges. This study first presents the general building energy situation. The state of the art of the energy in buildings is then reviewed, followed by pointing out the intelligent monitoring-based future direction, and then the final goal towards the smart city can be expected. Specifically, more than one hundred published papers are selected for sample analysis, taking into account different research topics and different publication dates etc. The research topics, research methods and research conclusions of these published papers are very different, and they have not yet produced results that could be generally accepted. Actually, most of the published papers focus on the analysis and conservation of building energy, including the energy model for analysis and prediction, the energy affected by resident behavior and building forms, the renewable energy utilization and zero energy building. While a small part of the published papers is concerned with the resilient structural energy dissipation and collapse-resistant. Furthermore, the intelligent monitoring of building energy, supported by advanced sensor development and big data analysis technology, is also providing us a more promising future on the way to the smart city. It should be further noted that the design and construction codes or standards related to building energy have not yet been retrieved, and these have a strong guiding significance for engineering practice. Therefore, more research needs to be done to expect a better practical outcome.

Environmental deterioration, renewable energy, natural resource rents, and schooling in Türkiye: Does the degree of energy transition matter for environmental quality?

Within the literature on energy and environmental economics, it is generally acknowledged that renewable energy can improve environmental quality; however, certain papers suggest that an optimal level of the usage of renewable energy sources may exist. Consequently, the utilization of renewable energy sources can result in environmental degradation up to a certain threshold. Then, environmental quality can be enhanced through the continued application of renewables. This indicates that the link between renewable energy and environmental devastation is inverted U-shaped. This paper presents empirical evidence concerning this possible association between renewable energy and environmental destruction in Türkiye, a country where fossil energy predominates in the energy mix. Additionally, the paper investigates the environmental influences of natural resource rents and schooling. This study utilizes annual data from 1971 to 2020 and implements time series methodologies that rely on the Fourier approximation. The paper thus accounts for an undetermined quantity of structural breaks. The results suggest that an inverted U-shaped link occurs between renewable energy and environmental destruction, signifying renewable energy initially contributes to a diminution in environmental quality before subsequently improving it. Additionally, environmental quality is positively associated with natural resource rents and negatively associated with schooling, according to the findings. Furthermore, the findings reveal that schooling worsens the combined effect of renewable energy on environmental degradation. These conclusions are discussed in the paper.

An enhanced jellyfish search optimizer for stochastic energy management of multi-microgrids with wind turbines, biomass and PV generation systems considering uncertainty.

The energy management (EM) solution of the multi-microgrids (MMGs) is a crucial task to provide more flexibility, reliability, and economic benefits. However, the energy management (EM) of the MMGs became a complex and strenuous task with high penetration of renewable energy resources due to the stochastic nature of these resources along with the load fluctuations. In this regard, this paper aims to solve the EM problem of the MMGs with the optimal inclusion of photovoltaic (PV) systems, wind turbines (WTs), and biomass systems. In this regard, this paper proposed an enhanced Jellyfish Search Optimizer (EJSO) for solving the EM of MMGs for the 85-bus MMGS system to minimize the total cost, and the system performance improvement concurrently. The proposed algorithm is based on the Weibull Flight Motion (WFM) and the Fitness Distance Balance (FDB) mechanisms to tackle the stagnation problem of the conventional JSO technique. The performance of the EJSO is tested on standard and CEC 2019 benchmark functions and the obtained results are compared to optimization techniques. As per the obtained results, EJSO is a powerful method for solving the EM compared to other optimization method like Sand Cat Swarm Optimization (SCSO), Dandelion Optimizer (DO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and the standard Jellyfish Search Optimizer (JSO). The obtained results reveal that the EM solution by the suggested EJSO can reduce the cost by 44.75% while the system voltage profile and stability are enhanced by 40.8% and 10.56%, respectively.

Enhancing residential energy access with optimized stand-alone hybrid solar-diesel-battery systems in Buea, Cameroon.

This study examined the optimal size of an autonomous hybrid renewable energy system (HRES) for a residential application in Buea, located in the southwest region of Cameroon. Two hybrid systems, PV-Battery and PV-Battery-Diesel, have been evaluated in order to determine which was the better option. The goal of this research was to propose a dependable, low-cost power source as an alternative to the unreliable and highly unstable electricity grid in Buea. The decision criterion for the proposed HRES was the cost of energy (COE), while the system's dependability constraint was the loss of power supply probability (LPSP). The crayfish optimization algorithm (COA) was used to optimize the component sizes of the proposed HRES, and the results were contrasted to those obtained from the whale optimization algorithm (WOA), sine cosine algorithm (SCA), and grasshopper optimization algorithm (GOA). The MATLAB software was used to model the components, criteria, and constraints of this single-objective optimization problem. The results obtained after simulation for LPSP of less than 1% showed that the COA algorithm outperformed the other three techniques, regardless of the configuration. Indeed, the COE obtained using the COA algorithm was 0.06%, 0.12%, and 1% lower than the COE provided by the WOA, SCA, and GOA algorithms, respectively, for the PV-Battery configuration. Likewise, for the PV-Battery-Diesel configuration, the COE obtained using the COA algorithm was 0.065%, 0.13%, and 0.39% lower than the COE provided by the WOA, SCA, and GOA algorithms, respectively. A comparative analysis of the outcomes obtained for the two configurations indicated that the PV-Battery-Diesel configuration exhibited a COE that was 4.32% lower in comparison to the PV-Battery configuration. Finally, the impact of the LPSP reduction on the COE was assessed in the PV-Battery-Diesel configuration. The decrease in LPSP resulted in an increase in COE owing to the nominal capacity of the diesel generator.

A techno-economic perspective on efficient hybrid renewable energy solutions in Douala, Cameroon's grid-connected systems.

Cameroon is currently grappling with a significant energy crisis, which is adversely affecting its economy due to cost, reliability, and availability constraints within the power infrastructure. While electrochemical storage presents a potential remedy, its implementation faces hurdles like high costs and technical limitations. Conversely, generator-based systems, although a viable alternative, bring their own set of issues such as noise pollution and demanding maintenance requirements. This paper meticulously assesses a novel hybrid energy system specifically engineered to meet the diverse energy needs of Douala, Cameroon. By employing advanced simulation techniques, especially the Hybrid Optimization Model for Electric Renewable (HOMER) Pro program, the study carefully examines the intricacies of load demands across distinct consumer categories while accommodating varied pricing models. The paper offers a detailed analysis of the proposed grid-connected PV/Diesel/Generator system, aiming to gauge its performance, economic feasibility, and reliability in ensuring uninterrupted energy supply. Notably, the study unveils significant potential for cost reduction per kilowatt-hour, indicating promising updated rates of $0.07/kW, $0.08/kW, and $0.06/kW for low, medium, and high usage groups, respectively. Furthermore, the research underscores the importance of overcoming operational challenges and constraints such as temperature fluctuations, equipment costs, and regulatory compliance. It also acknowledges the impact of operational nuances like maintenance and grid integration on system efficiency. As the world progresses towards renewable energy adoption and hybrid systems, this investigation lays a strong foundation for future advancements in renewable energy integration and energy management strategies. It strives to create a sustainable energy ecosystem in Cameroon and beyond, where hybrid energy systems play a pivotal role in mitigating power deficiencies and supporting sustainable development.

The Impact of Renewable Energy Development on Regional Carbon Emission Reduction: Based on the Spatio-Temporal Analysis of 30 Provinces in China.

The development of renewable energy has become an important means for the world to cope with climate change, ensure energy security, and protect the ecological environment. Using the panel data of 30 provinces in China from 2013 to 2021, this study used the mediating effect model and the spatial Durbin model (SDM) to explore the mechanism and spatial effects of renewable energy development on China's regional carbon emission reduction. The results show that: (1) Renewable energy development can help to reduce carbon emission intensity. (2) The results of mechanism analysis show that renewable energy development reduces carbon intensity by improving energy structure, promoting industrial structure optimization, and industrial structure upgrading. (3) The development of renewable energy can not only reduce the local carbon intensity but also have a positive spillover effect on the carbon intensity of neighboring regions. (4) Further analysis shows that the long-term effect of renewable energy development on carbon emissions is greater than the short-term effect. At the same time, the heterogeneity analysis shows that compared with the Yellow River basin, the development of renewable energy has a significant carbon emission reduction effect in the Yangtze River Economic Belt region. Energy-rich areas fall into the "resource curse", which makes the carbon emission reduction effect of renewable energy development not significant. This paper has certain reference significance for promoting reasonable decomposition between regions and formulating renewable energy development policies.